Capturing Peer Group Contexts: In Defense of Socio-Cognitive Mapping Strategies to Identify Children’s Peer Network Affiliations
Main Article Content
Abstract
Socio-Cognitive Mapping is an observational method to collect information about people’s social networks in settings in which participant observers know each other well, for example in school settings. Compared to traditional self-report data, observation reports make it possible to include (anonymized) network information about people who do not participate. In a series of papers, Neal and colleagues have criticized the methodology of Socio-Cognitive Mapping studies. However, the criticisms do not pertain to the data but only to a specific analysis program, SCM4, that was used in about 80% of the reviewed studies. To document their critiques, the authors introduce a new analysis strategy intended to correct some of the problems identified, and combine this with a promising new Community Detection method. They compare their results to SCM4 results and find in random simulations that, when using criteria that are more restrictive, fewer groups and fewer group members are identified. I highlight the extent to which the critique of the program is only justified under restrictive conditions, explain that the backbone of the proposed method has been used before, list problems of analyses that their method does not overcome, and outline avenues for their solution.
Article Details
How to Cite
KINDERMANN, Thomas A.
Capturing Peer Group Contexts: In Defense of Socio-Cognitive Mapping Strategies to Identify Children’s Peer Network Affiliations.
Medical Research Archives, [S.l.], v. 10, n. 9, sep. 2022.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/3149>. Date accessed: 25 apr. 2024.
doi: https://doi.org/10.18103/mra.v10i9.3149.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Moreno JL. Who Shall Survive? A New Approach to the Problem of Human Interrelations. Nervous and Mental Disease Publishing; 1933.
2. Freeman L. The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press; 2004.
3. Cairns RB, Cairns BD. Lifelines and Risks: Pathways of Youth in Our Time. Cambridge University Press; 1994.
4. Mehess S J. Finding the Missing Links: A Comparison of Social Network Analysis Methods. Unpublished Masters Thesis, Department of Psychology, Portland State University; 2016. https://pdxscholar.library.pdx.edu/open_access_etds/2728/
5. Hanneman RA, Riddle M. Introduction to Social Network Methods. University of California Riverside; 2005. http://faculty.ucr.edu/~hanneman/
6. Richards WD, Rice RE. The NEGOPY network analysis program. Soc Networks. 1981;3:215-223. https://doi.org/10.1016/0378-8733(81)90017-4
7. Krackhardt D. Cognitive social structures. Soc Networks. 1987;9:109-134. https://doi.org/10.1016/0378-8733(87)90009-8
8. Kindermann TA. Effects of naturally-existing peer groups on changes in academic engagement in a cohort of sixth graders. Child Dev. 2007;78:1186-1203.
9. Neal Z, Neal JW, Domagalski R. False positives using social cognitive mapping to identify children’s peer groups. Collabra: Psychol. 2021,7(1):1-14. https://doi.org/10.1525/collabra.17969
10. Neal JW, Neal ZP. The multiple meanings of peer groups in social cognitive mapping. Soc Dev. 2013 22:580–594. doi: 10.1111/j.1467-9507.2012.00656.x
11. Neal ZP, Neal JW. Opening the black box of social cognitive mapping. Soc Dev. 2013;22: 604–608.
doi: 10.1111/j.1467-9507.2012.00668.x
12. Cairns RB, Perrin JE, Cairns BD. Social structure and social cognition in early adolescence: Affiliative patterns. J Early Adolesc. 1985;5:339–355. https://doi.org/10.1177/0272431685053007
13. Kindermann TA, Kwee R. NETWORKS 3.5.01 [Computer program]. Portland State University, Department of Psychology. 1995. https://sites.google.com/site/sonetpsu/
14. Bakeman R. Computing lag sequential statistics: The ELAG program. Behav Res Methods Instrum Comput. 1983;15:530-535.
15. Kindermann TA. Natural peer groups as contexts for individuals’ development: The sample case of children's motivation in school. Dev Psychol. 1993;29: 970 977.
16. Gest SD, Moody J, Rulison K L. Density or distinction? The roles of data structure and group detection methods in describing adolescent peer groups. Journal Soc Struct. 2006;6. http://www.cmu.edu/joss/content/articles/volume8/GestMoody/
17. Molloy LE, Gest SD, Rulison KL. Peer influences on academic motivation: Exploring multiple methods of assessing youths’ most “influential” peer relationships. J Early Adolesc. 2011;31(1):13–40. DOI: 10.1177/0272431610384487
18. Gest SD, Graham-Bermann SA, Hartup WW. Peer experience: common and unique features of number of friendships, social network centrality, and sociometric status. Soc Dev. 2001;10:23-40.
19. Gest SD, Farmer T, Cairns BD, Xie H. Identifying children’s peer social networks in school classrooms: Links between peer reports and observed interactions. Soc Dev. 2003;12:513-529. https://doi.org/10.1111/1467-9507.00246
20. Neal ZP, Neal JW. Network analysis in community psychology: Looking back, looking forward . Am J Community Psychol. 2017;60 279–295. doi 10.1002/ajcp.12158
21. Neal JW, Neal ZP, Capella E. Seeing and being been: Predictors of accurate perceptions about classmates’ relationships. Soc Networks. 2016;44:1–8. doi:10.1016/j.socnet.2015.07.002
22. Breiger R. The Duality of Persons and Groups. Soc Forces. 1974:53(2):181-190.
23. Neal ZP. The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Soc Networks. 2014;39:84–97.
24. Weber M. The theory of social and economic organization. Free Press; 1947.
25. Kindermann TA. Strategies for the study of individual development within naturally existing peer groups. Soc Dev. 1996;5:158-173.
26. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46. doi:10.1177/001316446002000104.
27. Zarbatany L, Ellis WE, Chen X, Kinal M, Boyko L. The moderating role of clique hierarchical organization on resource control by central clique members. J Youth Adolesc. 2019;48:359–371. https://doi.org/10.1007/s10964-018-0972-9
28. Domagalski R, Neal Z P, Sagan B. Backbone: An R package for extracting the backbone of bipartite projections. PLoS ONE. 2021;16(1):e0244363. https://doi.org/10.1371/journal.pone.0244363
29. Kindermann TA, Gest SD. The peer group: Linking conceptualizations, theories, and methods. In Laursen B, Bukowski W, Rubin KH, eds. Handbook of peer interactions, relationships, and groups; 2nd Ed. Guilford; 2018,84-105.
30. Kindermann TA. Distinguishing "buddies" from "bystanders": The study of children's development within naturally existing peer contexts. In Kindermann TA, Valsiner J., eds. Development of person context relations. Erlbaum; 1995,205 226.
31. Mehta PD, Neale MC. People are variables too: Multilevel structural equations modeling. Psychological Methods. 2005;10:259–284. DOI: 10.1037/1082-989X.10.3.259
32. Cairns RB, Gariepy JL, Kindermann TA, Leung MC. A user manual for SCM 4.0. Unpublished manuscript, Center for Developmental Science, University of North Carolina at Chapel Hill. 1998.
33. Cairns RB, Gariepy J L, Kindermann TA. Identifying social clusters in natural settings. Unpublished manuscript, University of North Carolina at Chapel Hill, Department of Psychology. 1989.
34. Bacéte, FJG, Perrin GM. Social Cognitive Maps: Un método para identificar los grupos sociales en contextos naturales. Psychosocial Intervention. 2013;22(1):61–70. https://doi.org/10.5093/in2013a8
35. Farmer TW, Cairns RB. Social networks and social status in emotionally disturbed children. Behav Disord. 1991;16(4),288–298. https://doi.org/10.1177/019874299101600404
36. Farmer TW, Xie H. Manufacturing phenomena or preserving phenomena? Core issues in the identification of peer social groups with Social Cognitive Mapping procedures. Soc Dev. 2013; 22:595–603. doi: 10.1111/j.1467-9507.2012.00669
37. Farmer TW, Stuart CB, Lorch NH, Fields E. The social behavior and peer relations of emotionally and behaviorally disturbed students in residential treatment: A pilot study. J Emot Behav Disord. 1993;1(4):223–234. https://doi.org/10.1177/106342669300100404
38. Neal ZP. SCM: Stata Module to Process Data for Social Cognitive Mapping, Statistical Software Components S457446, Boston College Department of Economics. 2012. Handle: RePEc:boc:bocode:s457446, https://ideas.repec.org/c/boc/bocode/s457446.html
39. Wiedemann G, Niekler A. Hands-On: A five-day text mining course for humanists and social scientists in {R}. In Bockwinkel P, Declerck T, Kübler S, Zinsmeister H, eds.. Proceedings of the Workshop on Teaching {NLP} for Digital Humanities {Teach4DH@GSCL}). 2017:57-65. http://ceur-ws.org/Vol-1918/wiedemann.pdf
40. Srinivasan K, Currim F, Ram S. Predicting high-cost patients at point of admission Using network science. IEEE J Biomed Health Inform. 2019;22:1970-1977;
DOI: 10.1109/JBHI.2017.2783049
2. Freeman L. The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press; 2004.
3. Cairns RB, Cairns BD. Lifelines and Risks: Pathways of Youth in Our Time. Cambridge University Press; 1994.
4. Mehess S J. Finding the Missing Links: A Comparison of Social Network Analysis Methods. Unpublished Masters Thesis, Department of Psychology, Portland State University; 2016. https://pdxscholar.library.pdx.edu/open_access_etds/2728/
5. Hanneman RA, Riddle M. Introduction to Social Network Methods. University of California Riverside; 2005. http://faculty.ucr.edu/~hanneman/
6. Richards WD, Rice RE. The NEGOPY network analysis program. Soc Networks. 1981;3:215-223. https://doi.org/10.1016/0378-8733(81)90017-4
7. Krackhardt D. Cognitive social structures. Soc Networks. 1987;9:109-134. https://doi.org/10.1016/0378-8733(87)90009-8
8. Kindermann TA. Effects of naturally-existing peer groups on changes in academic engagement in a cohort of sixth graders. Child Dev. 2007;78:1186-1203.
9. Neal Z, Neal JW, Domagalski R. False positives using social cognitive mapping to identify children’s peer groups. Collabra: Psychol. 2021,7(1):1-14. https://doi.org/10.1525/collabra.17969
10. Neal JW, Neal ZP. The multiple meanings of peer groups in social cognitive mapping. Soc Dev. 2013 22:580–594. doi: 10.1111/j.1467-9507.2012.00656.x
11. Neal ZP, Neal JW. Opening the black box of social cognitive mapping. Soc Dev. 2013;22: 604–608.
doi: 10.1111/j.1467-9507.2012.00668.x
12. Cairns RB, Perrin JE, Cairns BD. Social structure and social cognition in early adolescence: Affiliative patterns. J Early Adolesc. 1985;5:339–355. https://doi.org/10.1177/0272431685053007
13. Kindermann TA, Kwee R. NETWORKS 3.5.01 [Computer program]. Portland State University, Department of Psychology. 1995. https://sites.google.com/site/sonetpsu/
14. Bakeman R. Computing lag sequential statistics: The ELAG program. Behav Res Methods Instrum Comput. 1983;15:530-535.
15. Kindermann TA. Natural peer groups as contexts for individuals’ development: The sample case of children's motivation in school. Dev Psychol. 1993;29: 970 977.
16. Gest SD, Moody J, Rulison K L. Density or distinction? The roles of data structure and group detection methods in describing adolescent peer groups. Journal Soc Struct. 2006;6. http://www.cmu.edu/joss/content/articles/volume8/GestMoody/
17. Molloy LE, Gest SD, Rulison KL. Peer influences on academic motivation: Exploring multiple methods of assessing youths’ most “influential” peer relationships. J Early Adolesc. 2011;31(1):13–40. DOI: 10.1177/0272431610384487
18. Gest SD, Graham-Bermann SA, Hartup WW. Peer experience: common and unique features of number of friendships, social network centrality, and sociometric status. Soc Dev. 2001;10:23-40.
19. Gest SD, Farmer T, Cairns BD, Xie H. Identifying children’s peer social networks in school classrooms: Links between peer reports and observed interactions. Soc Dev. 2003;12:513-529. https://doi.org/10.1111/1467-9507.00246
20. Neal ZP, Neal JW. Network analysis in community psychology: Looking back, looking forward . Am J Community Psychol. 2017;60 279–295. doi 10.1002/ajcp.12158
21. Neal JW, Neal ZP, Capella E. Seeing and being been: Predictors of accurate perceptions about classmates’ relationships. Soc Networks. 2016;44:1–8. doi:10.1016/j.socnet.2015.07.002
22. Breiger R. The Duality of Persons and Groups. Soc Forces. 1974:53(2):181-190.
23. Neal ZP. The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Soc Networks. 2014;39:84–97.
24. Weber M. The theory of social and economic organization. Free Press; 1947.
25. Kindermann TA. Strategies for the study of individual development within naturally existing peer groups. Soc Dev. 1996;5:158-173.
26. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46. doi:10.1177/001316446002000104.
27. Zarbatany L, Ellis WE, Chen X, Kinal M, Boyko L. The moderating role of clique hierarchical organization on resource control by central clique members. J Youth Adolesc. 2019;48:359–371. https://doi.org/10.1007/s10964-018-0972-9
28. Domagalski R, Neal Z P, Sagan B. Backbone: An R package for extracting the backbone of bipartite projections. PLoS ONE. 2021;16(1):e0244363. https://doi.org/10.1371/journal.pone.0244363
29. Kindermann TA, Gest SD. The peer group: Linking conceptualizations, theories, and methods. In Laursen B, Bukowski W, Rubin KH, eds. Handbook of peer interactions, relationships, and groups; 2nd Ed. Guilford; 2018,84-105.
30. Kindermann TA. Distinguishing "buddies" from "bystanders": The study of children's development within naturally existing peer contexts. In Kindermann TA, Valsiner J., eds. Development of person context relations. Erlbaum; 1995,205 226.
31. Mehta PD, Neale MC. People are variables too: Multilevel structural equations modeling. Psychological Methods. 2005;10:259–284. DOI: 10.1037/1082-989X.10.3.259
32. Cairns RB, Gariepy JL, Kindermann TA, Leung MC. A user manual for SCM 4.0. Unpublished manuscript, Center for Developmental Science, University of North Carolina at Chapel Hill. 1998.
33. Cairns RB, Gariepy J L, Kindermann TA. Identifying social clusters in natural settings. Unpublished manuscript, University of North Carolina at Chapel Hill, Department of Psychology. 1989.
34. Bacéte, FJG, Perrin GM. Social Cognitive Maps: Un método para identificar los grupos sociales en contextos naturales. Psychosocial Intervention. 2013;22(1):61–70. https://doi.org/10.5093/in2013a8
35. Farmer TW, Cairns RB. Social networks and social status in emotionally disturbed children. Behav Disord. 1991;16(4),288–298. https://doi.org/10.1177/019874299101600404
36. Farmer TW, Xie H. Manufacturing phenomena or preserving phenomena? Core issues in the identification of peer social groups with Social Cognitive Mapping procedures. Soc Dev. 2013; 22:595–603. doi: 10.1111/j.1467-9507.2012.00669
37. Farmer TW, Stuart CB, Lorch NH, Fields E. The social behavior and peer relations of emotionally and behaviorally disturbed students in residential treatment: A pilot study. J Emot Behav Disord. 1993;1(4):223–234. https://doi.org/10.1177/106342669300100404
38. Neal ZP. SCM: Stata Module to Process Data for Social Cognitive Mapping, Statistical Software Components S457446, Boston College Department of Economics. 2012. Handle: RePEc:boc:bocode:s457446, https://ideas.repec.org/c/boc/bocode/s457446.html
39. Wiedemann G, Niekler A. Hands-On: A five-day text mining course for humanists and social scientists in {R}. In Bockwinkel P, Declerck T, Kübler S, Zinsmeister H, eds.. Proceedings of the Workshop on Teaching {NLP} for Digital Humanities {Teach4DH@GSCL}). 2017:57-65. http://ceur-ws.org/Vol-1918/wiedemann.pdf
40. Srinivasan K, Currim F, Ram S. Predicting high-cost patients at point of admission Using network science. IEEE J Biomed Health Inform. 2019;22:1970-1977;
DOI: 10.1109/JBHI.2017.2783049